提出高效多任务暗光图像恢复模型，兼顾去模糊与增强。
DarkIR: Robust Low-Light Image Restoration
- 用新注意力机制增强轻量CNN的感知范围。
- 在三个数据集上达最新水平，计算开销更低。
- 适合实际夜景图像修复，代码开源可复现。
夜间或昏暗环境下的摄影常因光线不足和长曝光导致噪声大、亮度低、模糊等问题。尽管去模糊与暗光图像增强（LLIE）在此类场景下密切相关，现有方法多分别处理。本文提出一种高效且鲁棒的神经网络，用于多任务暗光图像恢复。不同于主流的Transformer架构，我们设计了新的注意力机制以增强轻量级CNN的感知范围，显著降低参数量与乘加操作数。所提模型DarkIR在LOLBlur、LOLv2和Real-LOLBlur三个主流数据集上均取得新最优性能，且能有效泛化至真实世界夜景图像。代码与模型已开源。
原文摘要 · Abstract (English)
Photography during night or in dark conditions typically suffers from noise, low light and blurring issues due to the dim environment and the common use of long exposure. Although Deblurring and Low-light Image Enhancement (LLIE) are related under these conditions, most approaches in image restoration solve these tasks separately. In this paper, we present an efficient and robust neural network for multi-task low-light image restoration. Instead of following the current tendency of Transformer-based models, we propose new attention mechanisms to enhance the receptive field of efficient CNNs. Our method reduces the computational costs in terms of parameters and MAC operations compared to previous methods. Our model, DarkIR, achieves new state-of-the-art results on the popular LOLBlur, LOLv2 and Real-LOLBlur datasets, being able to generalize on real-world night and dark images. Code and models at https://github.com/cidautai/DarkIR
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